Search Results (40)

Motor imagery (MI) is a widely used paradigm in brain–computer interface (BCI) systems, with applications in rehabilitation and neuroscience. In this study, magnetoencephalography (MEG) signals were employed to analyze MI and other mental imagery tasks. MEG provides high spatial resolution, facilitating the classification of imagery-related signals. This study aims to enhance the classification of motor and cognitive imagery (CI) tasks using a public MEG dataset including four distinct tasks: imagining the movement of hands (H) or feet (F), performing arithmetic subtraction (S), and forming words (W). MEG signals were decomposed using five signal-decomposition methods: Empirical Wavelet Transform (EWT), Maximal Overlap Discrete Wavelet Transform (MODWT), Empirical Mode Decomposition (EMD), Variational Mode Decomposition (VMD), and Multivariate Variational Mode Decomposition (MVMD). Feature extraction was performed using the Common Spatial Patterns (CSP), with t-test-based feature selection. Subsequently, commonly used machine learning algorithms were employed to classify MI and CI tasks. The results indicate that MVMD and MODWT achieved the highest accuracies when combined with the Artificial Neural Networks. MVMD yielded superior performances in (H and W: 79.2%; F and S: 75.8%; and F and W: 73.8%) tasks. MODWT achieved high accuracies in the H and W (75.9%) and F and W (76.3%) tasks. Overall, motor and non-motor pairs (H and W, F and W) yielded higher accuracy than the cognitive pair (W and S). Full article

Landslide displacement prediction is crucial for disaster mitigation, yet traditional methods often fail to capture the complex, non-stationary spatiotemporal dynamics of slope evolution. This study introduces an enhanced prediction framework that integrates multi-scale signal processing with dynamic, geology-aware graph modeling. The proposed methodology first employs the Maximum Overlap Discrete Wavelet Transform (MODWT) to denoise raw Global Navigation Satellite System (GNSS)-monitored displacement time series data, enhancing the underlying deformation features. Subsequently, a geology-aware graph is constructed, using the temporal correlation of displacement series as a practical proxy for physical relatedness between monitoring nodes. The framework’s core innovation lies in a dynamic graph optimization model with low-rank constraints, which adaptively refines the graph topology to reflect time-varying inter-sensor dependencies driven by factors like mining activities. Experiments conducted on a real-world dataset from an active open-pit mine demonstrate the framework’s superior performance. The DCRNN-proposed model achieved the highest accuracy among eight competing models, recording a Root Mean Square Error (RMSE) of 2.773 mm in the Vertical direction, a 39.1% reduction compared to its baseline. This study validates that the proposed dynamic graph optimization approach provides a robust and significantly more accurate solution for landslide prediction in complex, real-world engineering environments. Full article

Wind data are often cyclostationary due to cyclic variations, non-constant variance resulting from fluctuating weather conditions, and structural breaks due to transient behaviour (due to wind gusts and turbulence), resulting in unreliable wind power supply. In wavelet hybrid forecasting, wind prediction accuracy depends heavily on the decomposition level ($L$) and the wavelet filter technique selected. Hence, we examined the efficacy of wind predictions as a function of $L$and wavelet filters. In the proposed hybrid approach, differential evolution (DE) optimises the decomposition level of various wavelet filters (i.e., least asymmetric (LA), Daubechies (DB), and Morris minimum-bandwidth (MB)) using the maximal overlap discrete wavelet transform (MODWT), allowing for the decomposition of wind data into more statistically sound sub-signals. These sub-signals are used as inputs into the gated recurrent unit (GRU) to accurately capture wind speed. The final predicted values are obtained by reconciling the sub-signal predictions using multiresolution analysis (MRA) to form wavelet-MODWT-GRUs. Using wind data from three Wind Atlas South Africa (WASA) locations, Alexander Bay, Humansdorp, and Jozini, the root mean square error, mean absolute error, coefficient of determination, probability integral transform, pinball loss, and Dawid-Sebastiani showed that the MB-MODWT-GRU at $L=3$ was best across the three locations. Full article

The mid- and long-term hydrological forecast is important for water resource management and disaster prevention. Moreover, mid- and long-term hydrological forecasts in the region with poorly observed field meteorological data are a great challenge for traditional hydrological models due to the complexity of hydrological processes. To address this challenge, a machine learning model, particularly the deep learning model (DL), provides a new tool for improving the accuracy of runoff prediction. In this study, we took the Irtysh River, one of the longest rivers in Central Asia and a well-known trans-boundary river basin with poor field meteorological observations, as an example to develop a deep learning model based on LSTM and combined with runoff decomposition by Maximal Overlap Discrete Wavelet Transform (MODWT) to process target variables for predicting monthly streamflow. We also proposed an XGBoost-SHAP (Extreme Gradient Boost-SHapley Additive Explanations) method for the identification of predictors from large-scale indices for the streamflow forecast. The results suggest that MODWT shows the robustness of runoff decomposition between the training and test period. The deep learning model combined with MODWT shows better performance than the benchmark deep learning model without MODWT illustrated by an increased NSE. The XGBoost-SHAP method well identified the nonlinear relationship between the predictors and streamflow, and the predictors determined by XGBoost-SHAP can be physically explained. Compared with the traditional mutual information method, the XGBoost-SHAP method improves the accuracy of the deep learning model for streamflow forecast. The results of this study illustrate the ability of a deep learning model for mid- and long-term streamflow forecast, and the methods we developed in this study provide an effective approach to improve the streamflow prediction in the scarcely observed catchments. Full article

Heart rate variability (HRV), which is the variation between consecutive heartbeats, reflects the electrical activity of the heart and provides insight into the autonomic nervous system (ANS) function. This study uses wavelet transform-based HRV feature extraction to investigate cardiac sympatho-vagal balance. Both the continuous wavelet transform (CWT) and discrete wavelet transform (DWT) methods were applied in different steps. DWT was used for R-peak detection and CWT and MODWT were used to generate spectrograms from RR intervals. Frequency components (HF, LF, and VLF) within 0.003–0.4 Hz were extracted, and power estimation was performed. The LF/HF ratio, indicating sympatho-vagal balance, was calculated. ECG data from 42 arrhythmia patients and 18 normal sinus rhythm subjects were analyzed. The results showed a lower LF/HF ratio in arrhythmia patients, with higher HF power in arrhythmia subjects and higher LF power in normal sinus rhythm subjects. The study suggests that the parasympathetic nervous system dominates the ANS in arrhythmia patients, while the sympathetic nervous system dominates in normal sinus rhythm patients. Full article

To develop agricultural risk management strategies, the early identification of water deficits during the growing cycle is critical. This research proposes a deep learning hybrid approach for multi-step soil moisture forecasting in the Bundaberg region in Queensland, Australia, with predictions made for 1-day, 14-day, and 30-day, intervals. The model integrates Geospatial Interactive Online Visualization and Analysis Infrastructure (Giovanni) satellite data with ground observations. Due to the periodicity, transience, and trends in soil moisture of the top layer, time series datasets were complex. Hence, the Maximum Overlap Discrete Wavelet Transform (moDWT) method was adopted for data decomposition to identify the best correlated wavelet and scaling coefficients of the predictor variables with the target top layer moisture. The proposed 3-phase hybrid moDWT-Lasso-LSTM model used the Least Absolute Shrinkage and Selection Operator (Lasso) method for feature selection. Optimal hyperparameters were identified using the Hyperopt algorithm with deep learning LSTM method. This proposed model’s performances were compared with benchmarked machine learning (ML) models. In total, nine models were developed, including three standalone models (e.g., LSTM), three integrated feature selection models (e.g., Lasso-LSTM), and three hybrid models incorporating wavelet decomposition and feature selection (e.g., moDWT-Lasso-LSTM). Compared to alternative models, the hybrid deep moDWT-Lasso-LSTM produced the superior predictive model across statistical performance metrics. For example, at 1-day forecast, The moDWT-Lasso-LSTM model exhibits the highest accuracy with the highest $R^2\approx 0.92469$ and the lowest RMSE $\approx 0.97808$, MAE $\approx 0.76623$, and SMAPE $\approx 4.39700$%, outperforming other models. The moDWT-Lasso-DNN model follows closely, while the Lasso-ANN and Lasso-DNN models show lower accuracy with higher RMSE and MAE values. The ANN and DNN models have the lowest performance, with higher error metrics and lower R2 values compared to the deep learning models incorporating moDWT and Lasso techniques. This research emphasizes the utility of the advanced complementary ML model, such as the developed moDWT-Lasso-LSTM 3-phase hybrid model, as a robust data-driven tool for early forecasting of soil moisture. Full article

The growing prominence of generating a well-diversified portfolio by holding securities from multi-asset markets has, over the years, drawn criticism. Various financial market events have caused asset markets to co-move, especially in emerging markets, which reduces portfolio diversification and enhances return losses. Consequently, this study examines the time–frequency co-movement of multi-asset classes in South Africa by using the Multivariate Generalized Autoregressive Conditional Heteroscedastic–Asymmetrical Dynamic Conditional Correlation (MGARCH-DCC) model, Maximal Overlap Discrete Wavelet Transformation (MODWT), and the Continuous Wavelet Transform (WTC) for the period 2007 to 2024. The findings demonstrate that the equity–bond, equity–property, equity–gold, bond–property, bond–gold, and property–gold markets depict asymmetrical time-varying correlations. Moreover, correlation in these asset pairs varies at investment periods (short-term, medium-term, and long-term), with historical events such as the 2007/2008 Global Financial Crisis (GFC) and the COVID-19 pandemic causing these asset pairs to co-move at different investment periods, which reduces diversification properties. The findings suggest that South African multi-asset markets co-move, affecting the diversification properties of holding multi-asset classes in a portfolio at different investment periods. Consequently, investors should consider the holding periods of each asset market pair in a portfolio as they dictate the level of portfolio diversification. Investors should also remember that there are lead–lag relationships and risk transmission between asset market pairs, enhancing portfolio volatility. This study assists investors in making more informed investment decisions and identifying optimal entry or exit points within South African multi-asset markets. Full article

This paper is concerned with an end-to-end electrocardiogram (ECG) signal transformation from a continuous-wave (CW) radar signal using a specialized deep learning model. For this purpose, the presented deep learning model is designed using convolutional neural networks (CNNs) and bidirectional long short-term memory (Bi-LSTM) with a maximum-overlap discrete wavelet transform (MODWT) layer and an adaptive neuro-fuzzy network (ANFN) layer. The proposed method has the advantage of developing existing deep networks and machine learning to reconstruct signals through CW radars to acquire ECG biological information in a non-contact manner. The fully connected (FC) layer of the CNN is replaced by an ANFN layer suitable for resolving black boxes and handling complex nonlinear data. The MODWT layer is activated via discrete wavelet transform frequency decomposition with maximum-overlap to extract ECG-related frequency components from radar signals to generate essential information. In order to evaluate the performance of the proposed model, we use a dataset of clinically recorded vital signs with a synchronized reference sensor signal measured simultaneously. As a result of the experiment, the performance is evaluated by the mean squared error (MSE) between the measured and reconstructed ECG signals. The experimental results reveal that the proposed model shows good performance in comparison to the existing deep learning model. From the performance comparison, we confirm that the ANFN layer preserves the nonlinearity of information received from the model by replacing the fully connected layer used in the conventional deep learning model. Full article

We employ wavelet analysis using the maximum overlap discrete wavelet transform (MODWT) to examine the return and volatility interconnectedness between the German equity market (a prominent representative of the Eurozone market) and the BRICS countries over the period 2005–2017. Specifically, we investigate the presence of the pure form of financial contagion in the stock markets of Brazil, Russia, India, China, and South Africa subsequent to the Eurozone Sovereign Debt Crisis (EZDC). Our results indicate the presence of financial contagion between the Eurozone equity market and its counterparts in South Africa and Russia, characterised by co-movement and volatility spillover effects. This contagion is particularly evident at higher frequencies, suggesting that the transmission of shocks occurs rapidly across these markets in the short term. No financial contagion is observed in the Brazilian, Chinese, and Indian stock markets during the European Sovereign Debt Crisis. The absence of financial contagion observed in these three BRICS countries during the European Sovereign Debt Crisis suggests that policymakers in these countries should prioritise addressing idiosyncratic shock channels. Full article

This paper examines the volatility risk in the KSA stock market (Tadawul), with a specific focus on predicting volatility using the logarithm of the standard deviation of stock market prices (LSCP) as the output variable. To enhance volatility prediction, it proposes the combined use of the dynamic evolving neural fuzzy inference system (DENFIS) and the nonlinear spectral model, maximum overlapping discrete wavelet transform (MODWT). This study utilizes a dataset comprising 4609 observations and investigates the inputs of lag 1 of the close stock price (LCP), the natural logarithm of oil price (Loil), the natural logarithm of cost of living (LCL), and the interbank rate (IB), determined through autocorrelation (AC), partial autocorrelation (PAC), correlation, and Granger causality tests. Regression analysis reveals significant effects of variables on LSCP: LCP has a negative effect, and Loil has a positive effect in the ordinary least square (OLS) model, while LCL and IB have positive effects in the fixed effect model and negative effects in the random effect model. The MODWT-Haar-DENFIS model was developed as we found that the model has the potential to be an effective model for stock market forecasting. The results provide valuable insights for investors and policymakers, aiding in risk management, investment decisions, and the development of measures to mitigate stock market volatility. Full article

This study investigated the impact of the June 2015 geomagnetic storms on the Brazilian equatorial and low-latitude ionosphere by analyzing various data sources, including solar wind parameters from the advanced compositional explorer satellite (ACE), global positioning satellite vertical total electron content (GPS-VTEC), geomagnetic data, and validation of the SAMI2 model-VTEC with GPS-VTEC. The effect of geomagnetic disturbances on the Brazilian longitudinal sector was examined by applying multiresolution analysis (MRA) of the maximum overlap discrete wavelet transform (MODWT) to isolate the diurnal component of the disturbance dynamo (Ddyn), DP2 current fluctuations from the ionospheric electric current disturbance (Diono), and semblance cross-correlation wavelet analysis for local phase comparison between the Sq and Diono currents. Our findings revealed that the significant fluctuations in DP2 at the Brazilian equatorial stations (Belem, dip lat: −0.47° and Alta Floresta, dip lat: −3.75°) were influenced by IMF Bz oscillations; the equatorial electrojet also fluctuated in tandem with the DP2 currents, and dayside reconnection generated the field-aligned current that drove the DP2 current system. The short-lived positive ionospheric storm during the main phase on 22 June in the Southern Hemisphere in the Brazilian sector was caused by the interplay between the eastward prompt penetration of the magnetospheric convection electric field and the westward disturbance dynamo electric field. The negative ionospheric storms that occurred during the recovery phase from 23 to 29 June 2015, were attributed to the westward disturbance dynamo electric field, which caused the downward $\mathrm{E} \times \mathrm{B}$ drift of the plasma to a lower height with a high recombination rate. The comparison between the SAMI2 model-VTEC and GPS-VTEC indicates that the SAMI2 model underestimated the VTEC within magnetic latitudes of −9° to −24° in the Brazilian longitudinal sector from 6 to 17 June 2015. However, it demonstrated satisfactory agreement with the GPS-VTEC within magnetic latitudes of −9° to 10° from 8 to 15 June 2015. Conversely, the SAMI2 model overestimated the VTEC between ±10° magnetic latitudes from 16 to 28 June 2015. The most substantial root mean square error (RMSE) values, notably 10.30 and 5.48 TECU, were recorded on 22 and 23 June 2015, coinciding with periods of intense geomagnetic disturbance. Full article

Lamb-wave-based structural health monitoring is widely acknowledged as a reliable method for damage identification, classification, localization and quantification. However, due to the complexity of Lamb wave signals, especially after interacting with structural components and defects, interpreting these waves and extracting useful information about the structure’s health is still a major challenge. Deep-learning-based strategy offers a great opportunity to address such challenges as the algorithm can operate directly on raw discrete time-domain signals. Unlike traditional methods, which often require careful feature engineering and preprocessing, deep learning can automatically extract relevant features from the raw data. This paper proposes an autoencoder based on a bidirectional long short-term memory network (Bi-LSTM) with maximal overlap discrete wavelet transform (MODWT). layer to detect the signal anomaly and determine the location of the damage in the composite structure. MODWT decomposes the signal into multiple levels of detail with different frequency resolution, capturing both temporal and spectral features simultaneously. Comparing with vanilla Bi-LSTM, this approach enables the model to greatly enhance its ability to detect and locate structural damage in structures, thereby increasing safety and efficiency. Full article

This empirical research endeavor seeks to enhance the accuracy of forecasting time series data in the banking sector by utilizing data from the Amman Stock Exchange (ASE). The study relied on daily closed price index data, spanning from October 2014 to December 2022, encompassing a total of 2048 observations. To attain statistically significant results, the research employs various mathematical techniques, including the non-linear spectral model, the maximum overlapping discrete wavelet transform (MODWT) based on the Coiflet function (C6), and the autoregressive integrated moving average (ARIMA) model. Notably, the study’s findings encompass the comprehensive explanation of all past events within the specified time frame, alongside the introduction of a novel forecasting model that amalgamates the most effective MODWT function (C6) with a tailored ARIMA model. Furthermore, this research underscores the effectiveness of MODWT in decomposing stock market data, particularly in identifying significant events characterized by high volatility, which thereby enhances forecasting accuracy. These results hold valuable implications for researchers and scientists across various domains, with a particular relevance to the fields of business and health sciences. The performance evaluation of the forecasting methodology is based on several mathematical criteria, including the mean absolute percentage error (MAPE), the mean absolute scaled error (MASE), and the root mean squared error (RMSE). Full article

Atrial fibrillation is a common heart rhythm disorder that is now becoming a significant healthcare challenge as it affects more and more people in developed countries. This paper proposes a novel approach for detecting this disease. For this purpose, we examined the ECG signal by detecting QRS complexes and then selecting 30 successive R-peaks and analyzing the atrial activity segment with a variety of indices, including the entropy change, the variance of the wavelet transform indices, and the distribution of energy in bands determined by the dual-Q tunable Q-factor wavelet transform and coefficients of the Hilbert transform of ensemble empirical mode decomposition. These transformations provided a vector of 21 features that characterized the relevant part of the electrocardiography signal. The MIT-BIH Atrial Fibrillation Database was used to evaluate the proposed method. Then, using the K-fold cross-validation method, the sets of features were fed into the LS-SVM and SVM classifiers and a trilayered neural network classifier. Training and test subsets were set up to avoid sampling from a single participant and to maintain the balance between classes. In addition, individual classification quality scores were analyzed for each signal to determine the dependencies of the classification quality on the subject. The results obtained during the testing procedure showed a sensitivity of 98.86%, a positive predictive value of 99.04%, and a classification accuracy of 98.95%. Full article

In this paper, a machine learning (ML) approach to estimate blood pressure (BP) using photoplethysmography (PPG) is presented. The final aim of this paper was to develop ML methods for estimating blood pressure (BP) in a non-invasive way that is suitable in a telemedicine health-care monitoring context. The training of regression models useful for estimating systolic blood pressure (SBP) and diastolic blood pressure (DBP) was conducted using new extracted features from PPG signals processed using the Maximal Overlap Discrete Wavelet Transform (MODWT). As a matter of fact, the interest was on the use of the most significant features obtained by the Minimum Redundancy Maximum Relevance (MRMR) selection algorithm to train eXtreme Gradient Boost (XGBoost) and Neural Network (NN) models. This aim was satisfactorily achieved by also comparing it with works in the literature; in fact, it was found that XGBoost models are more accurate than NN models in both systolic and diastolic blood pressure measurements, obtaining a Root Mean Square Error (RMSE) for SBP and DBP, respectively, of 5.67 mmHg and 3.95 mmHg. For SBP measurement, this result is an improvement compared to that reported in the literature. Furthermore, the trained XGBoost regression model fulfills the requirements of the Association for the Advancement of Medical Instrumentation (AAMI) as well as grade A of the British Hypertension Society (BHS) standard. Full article